These metrics and processes should track performances and identify areas of improvement, including those related to data quality, model accuracy and operational metrics such as uptime and latency. 4.

Data rarely comes in usable form. Data wrangling and exploratory data analysis are the difference between a good data science model and garbage in, garbage out.

Workflow behind the MPT model. In this approach, the model learns from various material properties simultaneously in an initial training phase, followed by fine-tuning on the target property data ...

DVC, the underlying open-source technology behind the extension, brings agility, reproducibility, and collaboration into the existing data science workflow.

One Model customers can integrate different data sources and data destinations to create reports, dashboards and visualizations. They also gain access to One AI, One Model’s data science suite ...

AlikeAudience, a leading data science company specializing in omnichannel data activation, today announced the launch of AURAR, a groundbreaking data collaboration tool, with a native Lookalike model ...

Pecan AI, the leader in AI-based predictive analytics for BI analysts and business teams, today announced the addition of one-click model deployment a ...

On Sept. 22, Autodesk launched a new model coordination workflow between two of its most popular clash detection and design review tools—Autodesk Navisworks and BIM 360 Model Coordination.